Department of Pediatrics, Faculty of Medicine and University Hospital, Masaryk University, Brno, Czech Republic.
Department of Pediatric Neurology, Faculty of Medicine and University Hospital, Masaryk University, Černopolní 9, Brno, 612 00, Czech Republic.
Sci Rep. 2023 Oct 13;13(1):17372. doi: 10.1038/s41598-023-43599-5.
Our goal was to identify highly accurate empirical models for the prediction of the risk of febrile seizure (FS) and FS recurrence. In a prospective, three-arm, case-control study, we enrolled 162 children (age 25.8 ± 17.1 months old, 71 females). Participants formed one case group (patients with FS) and two control groups (febrile patients without seizures and healthy controls). The impact of blood iron status, peak body temperature, and participants' demographics on FS risk and recurrence was investigated with univariate and multivariate statistics. Serum iron concentration, iron saturation, and unsaturated iron-binding capacity differed between the three investigated groups (p < 0.05). These serum analytes were key variables in the design of novel multivariate linear mixture models. The models classified FS risk with higher accuracy than univariate approaches. The designed bi-linear classifier achieved a sensitivity/specificity of 82%/89% and was closest to the gold-standard classifier. A multivariate model assessing FS recurrence provided a difference (p < 0.05) with a separating sensitivity/specificity of 72%/69%. Iron deficiency, height percentile, and age were significant FS risk factors. In addition, height percentile and hemoglobin concentration were linked to FS recurrence. Novel multivariate models utilizing blood iron status and demographic variables predicted FS risk and recurrence among infants and young children with fever.
我们的目标是确定预测热性惊厥 (FS) 风险和 FS 复发的高度准确的经验模型。在一项前瞻性、三臂、病例对照研究中，我们招募了 162 名儿童（年龄 25.8 ± 17.1 个月，71 名女性）。参与者形成了一个病例组（FS 患者）和两个对照组（无惊厥发热患者和健康对照组）。使用单变量和多变量统计方法研究了血液铁状态、体温峰值和参与者人口统计学特征对 FS 风险和复发的影响。三组之间的血清铁浓度、铁饱和度和未饱和铁结合能力存在差异（p<0.05）。这些血清分析物是设计新型多元线性混合模型的关键变量。这些模型对 FS 风险的分类准确性高于单变量方法。设计的双线性分类器的灵敏度/特异性为 82%/89%，与金标准分类器最接近。评估 FS 复发的多元模型提供了差异（p<0.05），其分离灵敏度/特异性为 72%/69%。缺铁、身高百分位数和年龄是 FS 的重要危险因素。此外，身高百分位数和血红蛋白浓度与 FS 复发有关。利用血液铁状态和人口统计学变量的新型多元模型预测了发热婴儿和幼儿的 FS 风险和复发。
